The time-frequency representation of speech-like signal is suitable to be used with 1-D CNN, popularly known as TDNN in speech processing literature. Our method is inspired by the TDNN-based x-vector system [1]} developed for speaker verification task. This processes speech information at frame and segment level. In frame level, the TDNN captures contextual information by applying kernel over adjacent frames and by processing each speech frame in an identical manner. This also applies dilation in the temporal domain to reduce redundancy and to make it computationally efficient. The frame-level information is processed with several TDNN layers having different kernel sizes and dilation parameters. Finally, temporal pooling aggregates frame-level information into segment-level and this is followed by processing with fully connected (FC) and softmax layer for classification objective. The standard x-vector system computes the segment-level intermediate representation referred as embeddings which are further processed with another system for classification. In contrast, our proposed method trains the network in an end-to-end fashion for which the emotion for a test speech is obtained from the output of the trained network.
